The 3D point convolution branch: The 3D branch aims to extract structural features from the sparse points. This is difficult for 2D convolutions that operate on local neighbors as 3D points are located on an irregular grid. Therefore, we utilize the feature-kernel alignment convolution (FKAConv) [1]} that operates directly on 3D points to avoid this problem. The key idea of the FKAConv is to learn a linear transformation to align the neighboring points with the grid-like kernel. After that, it performs a weighted sum between this kernel and the features of the 3D points. One can see that 2D convolution is a special case where the learned linear transformation is always an identity matrix.
